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Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.

Human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs) show immense promise for patient-specific disease modeling, cardiotoxicity screening, and regenerative therapy development. However, thus far, hPSC-CMs in culture have not recapitulated the structural or functional properties of adult CMs in vivo. To gain global insight into hPSC-CM biology, we established a multiomics method for analyzing the hPSC-CM metabolome and proteome from the same cell culture, creating multidimensional profiles of hPSC-CMs. Specifically, we developed a sequential extraction to capture metabolites and proteins from the same hPSC-CM monolayer cultures and analyzed these extracts using high-resolution mass spectrometry. Using this method, we annotated 205 metabolites/lipids and 4319 proteins from 106 cells with high reproducibility. We further integrated the proteome and metabolome measurements to create network profiles of molecular phenotypes for hPSC-CMs. Out of 310 pathways identified using metabolomics and proteomics, 40 pathways were considered significantly overrepresented (false-discovery-rate-corrected p ≤ 0.05). Highly populated pathways included those involved in protein synthesis (ribosome, spliceosome), ATP generation (oxidative phosphorylation), and cardiac muscle contraction. This multiomics method achieves a deep coverage of metabolites and proteins, creating a multidimensional view of the hPSC-CM phenotype, which provides a strong technological foundation to advance the understanding of hPSC-CM biology. Raw data are available in the MassIVE repository with identifier MSV000088010.

Three-dimensional (3D) human induced pluripotent stem cell-derived engineered cardiac tissues (hiPSC-ECTs) have emerged as a promising alternative to two-dimensional hiPSC-cardiomyocyte monolayer systems because hiPSC-ECTs are a closer representation of endogenous cardiac tissues and more faithfully reflect the relevant cardiac pathophysiology. The ability to perform functional and molecular assessments using the same hiPSC-ECT construct would allow for more reliable correlation between observed functional performance and underlying molecular events, and thus is critically needed. Herein, for the first time, we have established an integrated method that permits sequential assessment of functional properties and top-down proteomics from the same single hiPSC-ECT construct. We quantitatively determined the differences in isometric twitch force and the sarcomeric proteoforms between two groups of hiPSC-ECTs that differed in the duration of time of 3D-ECT culture. Importantly, by using this integrated method we discovered a new and strong correlation between the measured contractile parameters and the phosphorylation levels of alpha-tropomyosin between the two groups of hiPSC-ECTs. The integration of functional assessments together with molecular characterization by top-down proteomics in the same hiPSC-ECT construct enables a holistic analysis of hiPSC-ECTs to accelerate their applications in disease modeling, cardiotoxicity, and drug discovery. Data are available via ProteomeXchange with identifier PXD022814.